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ORCIDArun Kumar 0001
Person information
- affiliation: University of California, San Diego, USA
- affiliation (PhD 2016): University of Wisconsin-Madison, WI, USA
Other persons with the same name
- Arun Kumar — disambiguation page
- Arun Kumar 0002
![[0000-0002-3513-3634]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-3513-3634")
— IBM Research-India, New Delhi, India - Arun Kumar 0003 — Polytechnic Institute of New York University, USA
- Arun Kumar 0004 — Sri Sathya Sai University, Prashanthi Nilayam, India
- Arun Kumar 0005 — Banaras Hindu University, Institute of Science, Department of Mathematics, Varanasi, India (and 1 more)
- Arun Kumar 0006 — National Institute of Technology Rourkela, India (and 3 more)
- Arun Kumar 0007 — Indian Institute of Technology Delhi, New Delhi, India
- Arun Kumar 0009 — University of Minnesota Twin Cities, USA
- Arun Kumar 0010 — RMIT University, Melbourne, Australia
- Arun Kumar 0011 — Universitat Oberta de Catalunya, Barcelona, Spain
- Arun Kumar 0012 — IFTM University, Moradabad, India
- Arun Kumar 0013 — NSIT University of Delhi, Division of Information Technology, CAITFS, India
- Arun Kumar 0014 — New Horizon College of Engineering, Department of Electronics and Communication, Bengaluru, India (and 1 more)
- Arun Kumar 0015 — G. B. Pant University of Agriculture and Technology, Department of Mathematics, Statistics and Computer Science, Uttarakhand, India
- Arun Kumar 0016 — Indian Institute of Technology, Department of Computer Science and Engineering, Indore, India
- Arun Kumar 0017 — University of Delhi, Department of Operational Research, India
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2020 – today
- 2024
[j32]Vraj Shah, Thomas J. Parashos, Arun Kumar:
How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses. Proc. VLDB Endow. 17(6): 1391-1404 (2024)- [e1]Sriraam Natarajan, Indrajit Bhattacharya, Richa Singh, Arun Kumar, Sayan Ranu, Kalika Bali, Abinaya K:
Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), Bangalore, India, January 4-7, 2024. ACM 2024 [contents] - 2023
- [j31]Yuhao Zhang, Arun Kumar:
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines. Proc. VLDB Endow. 16(11): 2728-2741 (2023) - [j30]Kabir Nagrecha, Arun Kumar:
Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads. Proc. VLDB Endow. 17(4): 712-725 (2023) - [c26]Yutong Shao, Arun Kumar, Ndapa Nakashole:
Database-Aware ASR Error Correction for Speech-to-SQL Parsing. ICASSP 2023: 1-5 - [i17]Xiuwen Zheng, Subhasis Dasgupta, Arun Kumar, Amarnath Gupta:
An Optimized Tri-store System for Multi-model Data Analytics. CoRR abs/2305.14391 (2023) - [i16]Kabir Nagrecha, Arun Kumar:
Saturn: An Optimized Data System for Large Model Deep Learning Workloads. CoRR abs/2309.01226 (2023) - [i15]Kabir Nagrecha, Arun Kumar:
Saturn: Efficient Multi-Large-Model Deep Learning. CoRR abs/2311.02840 (2023) - 2022
- [j29]Yutong Shao, Arun Kumar, Ndapa Nakashole:
Structured Data Representation in Natural Language Interfaces. IEEE Data Eng. Bull. 45(3): 68-81 (2022) - [j28]Arun Kumar, Alin Deutsch, Amarnath Gupta, Yannis Papakonstantinou, Babak Salimi, Victor Vianu:
Database Education at UC San Diego. SIGMOD Rec. 51(3): 43-46 (2022) - [j27]Arun Kumar, Alon Y. Halevy, Nesime Tatbul:
VLDB Scalable Data Science Category: The Inaugural Year. SIGMOD Rec. 51(3): 56-58 (2022) - [c25]Supun Nakandala, Arun Kumar:
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets. SIGMOD Conference 2022: 506-520 - 2021
- [j26]Arun Kumar:
Letter from the Rising Star Award Winner. IEEE Data Eng. Bull. 44(2): 94-95 (2021) - [j25]Supun Nakandala, Yuhao Zhang, Arun Kumar:
Errata for "Cerebro: A Data System for Optimized Deep Learning Model Selection". Proc. VLDB Endow. 14(6): 863 (2021) - [j24]Arun Kumar, Alon Y. Halevy, Nesime Tatbul:
Front Matter. Proc. VLDB Endow. 14(7) (2021) - [j23]Yuhao Zhang, Frank Mcquillan, Nandish Jayaram, Nikhil Kak, Ekta Khanna, Orhan Kislal, Domino Valdano, Arun Kumar:
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches. Proc. VLDB Endow. 14(10): 1769-1782 (2021) - [j22]Side Li, Arun Kumar:
Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning. Proc. VLDB Endow. 14(11): 2327-2340 (2021) - [j21]Liangde Li, Supun Chathuranga Nakandala, Arun Kumar:
Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration. Proc. VLDB Endow. 14(12): 2687-2690 (2021) - [j20]David Justo, Shaoqing Yi, Lukas Stadler, Nadia Polikarpova, Arun Kumar:
Towards A Polyglot Framework for Factorized ML. Proc. VLDB Endow. 14(12): 2918-2931 (2021) - [j19]Zack Ives, Johannes Gehrke, Jana Giceva, Arun Kumar, Rachel Pottinger:
VLDB Panel Summary: "The Future of Data(base) Education: Is the Cow Book Dead?". SIGMOD Rec. 50(3): 23-26 (2021) - [c24]Arun Kumar, Supun Nakandala, Yuhao Zhang, Side Li, Advitya Gemawat, Kabir Nagrecha:
Cerebro: A Layered Data Platform for Scalable Deep Learning. CIDR 2021 - [c23]Vraj Shah, Jonathan Lacanlale, Premanand Kumar, Kevin Yang, Arun Kumar:
Towards Benchmarking Feature Type Inference for AutoML Platforms. SIGMOD Conference 2021: 1584-1596 - [c22]Arun Kumar:
Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil? SIGMOD Conference 2021: 2878-2880 - [i14]Kabir Nagrecha, Arun Kumar:
Hydra: A System for Large Multi-Model Deep Learning. CoRR abs/2110.08633 (2021) - [i13]Xiuwen Zheng, Subhasis Dasgupta, Arun Kumar, Amarnath Gupta:
Processing Analytical Queries in the AWESOME Polystore [Information Systems Architectures]. CoRR abs/2112.00833 (2021) - 2020
- [j18]Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, Vijay Chidambaram:
Understanding and Benchmarking the Impact of GDPR on Database Systems. Proc. VLDB Endow. 13(7): 1064-1077 (2020) - [j17]Supun Nakandala, Yuhao Zhang, Arun Kumar:
Cerebro: A Data System for Optimized Deep Learning Model Selection. Proc. VLDB Endow. 13(11): 2159-2173 (2020) - [j16]Supun Nakandala, Arun Kumar, Yannis Papakonstantinou:
Query Optimization for Faster Deep CNN Explanations. SIGMOD Rec. 49(1): 61-68 (2020) - [j15]Supun Nakandala, Kabir Nagrecha, Arun Kumar, Yannis Papakonstantinou:
Incremental and Approximate Computations for Accelerating Deep CNN Inference. ACM Trans. Database Syst. 45(4): 16:1-16:42 (2020) - [c21]Supun Nakandala, Arun Kumar:
Vista: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale. SIGMOD Conference 2020: 1685-1700 - [c20]Vraj Shah, Side Li, Arun Kumar, Lawrence K. Saul:
SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference 2020: 2363-2374
2010 – 2019
- 2019
- [b1]Matthias Boehm, Arun Kumar, Jun Yang:
Data Management in Machine Learning Systems. Synthesis Lectures on Data Management, Morgan & Claypool Publishers 2019, ISBN 978-3-031-00741-5 - [j14]Allen Ordookhanians, Xin Li, Supun Nakandala, Arun Kumar:
Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations. Proc. VLDB Endow. 12(12): 1894-1897 (2019) - [j13]Yuhao Zhang, Arun Kumar:
Panorama: A Data System for Unbounded Vocabulary Querying over Video. Proc. VLDB Endow. 13(4): 477-491 (2019) - [j12]Wolfgang Gatterbauer, Arun Kumar:
Guest Editors' Introduction to the Special Section on the 33rd International Conference on Data Engineering (ICDE 2017). IEEE Trans. Knowl. Data Eng. 31(7): 1222-1223 (2019) - [c19]Jiue-An Yang, Jiayi Wang, Supun Nakandala, Arun Kumar, Marta M. Jankowska:
Predicting Eating Events in Free Living Individuals. eScience 2019: 627-629 - [c18]Jiue-An Yang, Jiayi Wang, Supun Nakandala, Arun Kumar, Marta M. Jankowska:
Predicting Eating Events in Free Living Individuals. eScience 2019: 648-649 - [c17]Supun Nakandala, Yuhao Zhang, Arun Kumar:
Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems. DEEM@SIGMOD 2019: 6:1-6:4 - [c16]Vraj Shah, Arun Kumar:
The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML. DEEM@SIGMOD 2019: 11:1-11:4 - [c15]Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Xi Wu, Jeffrey F. Naughton, Jignesh M. Patel:
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent. SIGMOD Conference 2019: 1517-1534 - [c14]Lingjiao Chen, Paraschos Koutris, Arun Kumar:
Towards Model-based Pricing for Machine Learning in a Data Marketplace. SIGMOD Conference 2019: 1535-1552 - [c13]Side Li, Lingjiao Chen, Arun Kumar:
Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra. SIGMOD Conference 2019: 1571-1588 - [c12]Supun Nakandala, Arun Kumar, Yannis Papakonstantinou:
Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations. SIGMOD Conference 2019: 1589-1606 - [c11]Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, Arun Kumar:
Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace. SIGMOD Conference 2019: 1885-1888 - [c10]Vraj Shah, Side Li, Kevin Yang, Arun Kumar, Lawrence K. Saul:
Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference 2019: 2001-2004 - [i12]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i11]Jiayi Wang, Jiue-An Yang, Supun Nakandala, Arun Kumar, Marta M. Jankowska:
Predicting Eating Events in Free Living Individuals - A Technical Report. CoRR abs/1908.05304 (2019) - [i10]Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, Vijay Chidambaram:
Understanding and Benchmarking the Impact of GDPR on Database Systems. CoRR abs/1910.00728 (2019) - 2018
- [j11]Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh:
In-RDBMS Hardware Acceleration of Advanced Analytics. Proc. VLDB Endow. 11(11): 1317-1331 (2018) - [i9]Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh:
In-RDBMS Hardware Acceleration of Advanced Analytics. CoRR abs/1801.06027 (2018) - [i8]Lingjiao Chen, Paraschos Koutris, Arun Kumar:
Model-based Pricing for Machine Learning in a Data Marketplace. CoRR abs/1805.11450 (2018) - 2017
- [j10]Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel:
Towards Linear Algebra over Normalized Data. Proc. VLDB Endow. 10(11): 1214-1225 (2017) - [j9]Vraj Shah, Arun Kumar, Xiaojin Zhu:
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? Proc. VLDB Endow. 11(3): 366-379 (2017) - [c9]Lingjiao Chen, Paraschos Koutris, Arun Kumar:
Model-based Pricing: Do Not Pay for More than What You Learn! DEEM@SIGMOD 2017: 1:1-1:4 - [c8]Dharmil Chandarana, Vraj Shah, Arun Kumar, Lawrence K. Saul:
SpeakQL: Towards Speech-driven Multi-modal Querying. HILDA@SIGMOD 2017: 11:1-11:6 - [c7]Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. SIGMOD Conference 2017: 1307-1322 - [c6]Arun Kumar, Matthias Boehm, Jun Yang:
Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD Conference 2017: 1717-1722 - [i7]Fengan Li, Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xi Wu:
When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of Accelerating Machine Learning using Coding. CoRR abs/1702.06943 (2017) - [i6]Vraj Shah, Arun Kumar, Xiaojin Zhu:
Stop That Join! Discarding Dimension Tables when Learning High Capacity Classifiers. CoRR abs/1704.00485 (2017) - 2016
- [j8]Ce Zhang, Arun Kumar, Christopher Ré:
Materialization Optimizations for Feature Selection Workloads. ACM Trans. Database Syst. 41(1): 2:1-2:32 (2016) - [c5]Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xiaojin Zhu:
To Join or Not to Join?: Thinking Twice about Joins before Feature Selection. SIGMOD Conference 2016: 19-34 - [i5]Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. CoRR abs/1606.04722 (2016) - [i4]Lingjiao Chen, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel:
Towards Linear Algebra over Normalized Data. CoRR abs/1612.07448 (2016) - 2015
- [j7]Arun Kumar, Mona Jalal, Boqun Yan, Jeffrey F. Naughton, Jignesh M. Patel:
Demonstration of Santoku: Optimizing Machine Learning over Normalized Data. Proc. VLDB Endow. 8(12): 1864-1867 (2015) - [j6]Arun Kumar, Robert McCann, Jeffrey F. Naughton, Jignesh M. Patel:
Model Selection Management Systems: The Next Frontier of Advanced Analytics. SIGMOD Rec. 44(4): 17-22 (2015) - [c4]Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel:
Learning Generalized Linear Models Over Normalized Data. SIGMOD Conference 2015: 1969-1984 - 2014
- [c3]Ce Zhang, Arun Kumar, Christopher Ré:
Materialization optimizations for feature selection workloads. SIGMOD Conference 2014: 265-276 - 2013
- [j5]Arun Kumar, Feng Niu, Christopher Ré:
Hazy: making it easier to build and maintain big-data analytics. Commun. ACM 56(3): 40-49 (2013) - [j4]Pradap Konda, Arun Kumar, Christopher Ré, Vaishnavi Sashikanth:
Feature Selection in Enterprise Analytics: A Demonstration using an R-based Data Analytics System. Proc. VLDB Endow. 6(12): 1306-1309 (2013) - [j3]Arun Kumar, Feng Niu, Christopher Ré:
Hazy: Making it Easier to Build and Maintain Big-data Analytics. ACM Queue 11(1): 30 (2013) - [c2]Michael R. Anderson, Dolan Antenucci, Victor Bittorf, Matthew Burgess, Michael J. Cafarella, Arun Kumar, Feng Niu, Yongjoo Park, Christopher Ré, Ce Zhang:
Brainwash: A Data System for Feature Engineering. CIDR 2013 - 2012
- [j2]Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar:
The MADlib Analytics Library or MAD Skills, the SQL. Proc. VLDB Endow. 5(12): 1700-1711 (2012) - [c1]Xixuan Feng, Arun Kumar, Benjamin Recht, Christopher Ré:
Towards a unified architecture for in-RDBMS analytics. SIGMOD Conference 2012: 325-336 - [i3]Xixuan Feng, Arun Kumar, Ben Recht, Christopher Ré:
Towards a Unified Architecture for in-RDBMS Analytics. CoRR abs/1203.2574 (2012) - [i2]Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar:
The MADlib Analytics Library or MAD Skills, the SQL. CoRR abs/1208.4165 (2012) - 2011
- [j1]Arun Kumar, Christopher Ré:
Probabilistic Management of OCR Data using an RDBMS. Proc. VLDB Endow. 5(4): 322-333 (2011) - [i1]Arun Kumar, Christopher Ré:
Probabilistic Management of OCR Data using an RDBMS. CoRR abs/1106.0718 (2011)
Coauthor Index
Supun Nakandala
aka: Supun Chathuranga Nakandala
aka: Supun Chathuranga Nakandala
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